Systems | Development | Analytics | API | Testing

Discover the groundbreaking convergence of Playwright and Generative AI that's transforming how web testing is approached. This session unveils a revolutionary capability where AI assists in automatically generating test scripts without requiring manual coding. Experience how this intelligent automation system can analyze your application, understand user flows, and create comprehensive test suites with minimal human intervention.

The rise of AI is transforming how software is developed and tested—but the hardest questions for QA leaders aren’t about tools. They’re about people, productivity, and long-term strategy. As organizations race to integrate AI, leaders must rethink how they hire, train, and guide teams while making tough decisions about whether to build custom solutions, buy off-the-shelf platforms, or take a more measured approach. This panel brings together leaders who are navigating these crossroads in real time.

AI in automation is rapidly emerging as a powerful enabler for testers. From automation script generation utilities to simplifying API testing and framework development, these capabilities promise to accelerate productivity. But as testers embrace assistants like Copilot and Cursor, big questions emerge - What does this mean to the future of open-source frameworks like Selenium and Playwright? How should testers balance the speed of AI-generated code with the need for reliability and maintainability? ⁠How do we measure productivity gains from AI-paired programming? And AI doesn't stop at coding.

In this fireside chat with Jaydeep, we’ll dive into how AI is changing the way we measure success in both QA processes and live generative AI bots. On the QA side, we’ll look at cycle time reduction—the “time goalie” metric that shows how quickly we move from discovering a bug to fixing it. We’ll also talk about predictive quality accuracy, which shifts QA from being reactive to proactive by predicting which code changes are most likely to introduce bugs. And of course, we’ll touch on test creation velocity—how much faster teams are able to create meaningful automation with AI’s support.

Model Context Protocol (MCP) lets AI agents use real tools safely. In this talk, we’ll see how Playwright MCP bridges agents and a real browser by leveraging the accessibility tree (not screenshots) to navigate pages, locate elements, perform actions, and extract data—then export stable Playwright tests you can commit.

The AI ecosystem is exploding with tools that promise to accelerate delivery, improve quality, and transform the way we work. Yet for many teams, evaluating these tools is overwhelming - flashy demos and marketing claims rarely answer the real questions: Will this work in our context? Can it scale? Is it sustainable?

In this session, Andrew will trace Vibe Coding's journey—from emergence to current impact—exploring how it has got us into re-thinking development and testing. He'll examine today's tools, real-world use cases, and the cultural shifts teams need to embrace this AI-driven approach. Andrew will share hot takes on myths versus reality and deliver practical advice for getting started. This video is of one of the sessions presented at - World’s Leading Virtual Software Testing Conference.

AI is leveling the field across organizations. Everyone, from leaders to individual contributors, is approaching work with a beginner’s mindset, questioning what AI can do, what it cannot, and how it affects business, customers, and roles. This shared uncertainty challenges traditional leadership models and raises a fundamental question. How does leadership evolve when no one has all the answers?

AI is reshaping software testing, but real quality does not come from hype alone. This session explores where AI truly adds value in automation and where human testers remain essential. It focuses on building a practical partnership between AI and testers, where speed and scale are balanced with judgment, context, and responsibility.

Retrieval-Augmented Generation can sound convincing while still being wrong. This session focuses on moving beyond surface-level metrics and turning stochastic AI outputs into evidence-backed, verifiable results. It explores how to test the entire RAG pipeline, from ingestion and indexing to retrieval, grounding, and answerability, ensuring every response is traceable to the right source, policy, and user context.